Climate models get smarter, but uncertainty just won’t go away

The projections in the new IPCC report won't be much more precise than the last.

It's been five years since the last report from the Intergovernmental Panel on Climate Change, and the organization is currently preparing its fifth assessment report (AR5). These reports provide both an update on what we've learned about the climate in the intervening years and projections for likely future climates based on that new understanding.

Those projections are powered by climate models. Starting with AR4, those projections were based on the work of the World Climate Research Programme. This group identifies the current climate models from a variety of institutions, and runs them under a variety of emissions scenarios. Then, the WCRP collects the results of multiple runs from the ensemble of climate models, and uses that to predict the likely climate change and remaining uncertainties.

You might expect the progress made during the intervening five years would greatly narrow the uncertainties since the last report. If so, get ready for disappointment. A pair of researchers from ETH Zurich has compared the results from AR4 with the ones that will be coming out in AR5, and they find that the uncertainties haven't gone down much. And, somewhat ironically, they blame the improvements—as researchers are able to add more factors to their models, each new factor comes with its own uncertainties, which keeps the models from narrowing in on a value.

The model comparisons are run by the Coupled Model Intercomparison Project, or CMIP ("coupled" refers to treating the ocean and atmosphere as a linked system). CMIP3, confusingly, was a run of the models to prepare for the IPCC AR4. The groups have since synchronized, and CMIP5 will be part of AR5.

The number of changes, some brought about by increased computing power, are staggering. More models are used, including some from entirely new sources. Additional forcings are included in many of them (at the time of AR3, several of the models assumed solar and volcanic forcings were constant). All models now include both the direct effects of aerosols (which reflect sunlight) as well as their indirect effects, including changes in clouds and precipitation. In addition, the models are now run at a much finer spatial resolution, since advances in computing power allow more calculations to be used in each model run.

An exact comparison between the two different sets of models is made challenging by the fact the IPCC has changed the details of its CO2 emissions scenarios. But, as best they could, the authors managed to compare the projections of the two sets of models.

When it comes to temperatures, the CMIP3 and 5 are in rough agreement as to the magnitude and regional details on future warming. But the areas where the models produce inconsistent results is actually larger in the updated version, with an area southeast of Greenland remaining difficult to predict all the way out to the end of the century. Predicted precipitation changes are similar, with areas of uncertain predictions growing as the century wore for both CMIP3 and 5 (they're concentrated around the equator). Although the new models seem to have fewer areas of uncertainty, they now have one sitting on top of a major agricultural area: North America's Great Plains.

How can we improve the models yet have them produce more uncertainty? The authors have a long list of reasons. Some of these are systemic: limited processing time, lack of detailed data to test the models against, and a lack of clear metrics to judge the success of a given model. Others focus more on the nature of climate change itself: forcings we haven't identified, and a degree of natural variation. But the authors suspect that at least some of the problem boils down to researchers using their increased computational capacity to add even more factors into their models:

In contrast to end users, who would define model quality on the basis of prediction accuracy, climate model developers often judge their models to be better if the processes are represented in more detail. Thus, the new models are likely to be better in the sense of being physically more plausible, but it is difficult to quantify the impact of that on projection accuracy

In other words, climatologists are building their models to best reflect what we know about the natural world. They're not building them to be the most effective means of making predictions for the future of specific regions of the globe. And, at least partly as a result, the models scientists use capture a great deal of uncertainty in our understanding of the climate. But that's a problem for policymakers, who could use specific predictions to act on.

In any case, given a similar set of emissions, the new set of models seems to behave very much like the old one, with temperatures rising by over 4°C by the end of the century when emissions continue unabated. Even when taking uncertainties into account, it's difficult to keep that change under 3°C without significant changes to our fossil fuels habit.

114 Reader Comments

An article on weather and no mention of chaos theory? Weather is a classic example of a chaotic system. Small changes in inputs can have big effects later on, while large changes don't necessarily change the system much.

You can refine the models and find generalized patterns, but you'll never be able to determine the specific weather forecast much into the future.

An article on weather and no mention of chaos theory? Weather is a classic example of a chaotic system. Small changes in inputs can have big effects later on, while large changes don't necessarily change the system much.

You can refine the models and find generalized patterns, but you'll never be able to determine the specific weather forecast much into the future.

This is not an article about weather, it is one about climate. Climate is long term averages including temperature, rain, snow and humidity over the seasons while weather is what is actually happening. It is possible to predict the climate years in advance but not weather.

An article on weather and no mention of chaos theory? Weather is a classic example of a chaotic system. Small changes in inputs can have big effects later on, while large changes don't necessarily change the system much.

You can refine the models and find generalized patterns, but you'll never be able to determine the specific weather forecast much into the future.

Weather isn't climate. Now, doubtless there ARE some dependencies on initial conditions in the climate as well, but my bet is that they will tend to damp out. Any starting conditions that are reasonable should fall within the same phase space.

No, the problem here is one of uncertainty about the different feedbacks. There are many of them, they are interrelated in complex ways, and small differences in the way they are modeled can result in fairly large differences in regional predictions.

An article on weather and no mention of chaos theory? Weather is a classic example of a chaotic system. Small changes in inputs can have big effects later on, while large changes don't necessarily change the system much.

You can refine the models and find generalized patterns, but you'll never be able to determine the specific weather forecast much into the future.

Weather isn't climate. Now, doubtless there ARE some dependencies on initial conditions in the climate as well, but my bet is that they will tend to damp out. Any starting conditions that are reasonable should fall within the same phase space.

No, the problem here is one of uncertainty about the different feedbacks. There are many of them, they are interrelated in complex ways, and small differences in the way they are modeled can result in fairly large differences in regional predictions.

You don't seem understand what he's saying. He's not saying that weather is climate, or vice-versa. He's saying that both weather and climate are chaotic systems resulting from the very complex non-linear feedbacks you, yourself, allude to. The problem in studying systems like these is not the underlying mechanisms—physics in this case—but these systems-level issues. Prediction is notoriously difficult because small changes in initial conditions lead to wide variation in output. Therefore these problems of increased uncertainty when the models have increasing sophistication.

It's interesting to note that models of chaotic systems with superior explanatory power may have weaker predictive power.

Edit: Chaotic systems don't "damp out" the effects of initial conditions, but the situation is not completely hopeless because of strange attractors. Note: planetary orbits are also chaotic but very predictable over the time frames we're normally interested in—we're currently in a relatively stable state space. The fear with climate is that we're forcing the climate out of a relatively stable state space into another. Whatever that new state space is, however, seems to have a high degree of uncertainty.

I wouldn't trust a climate model that doesn't have uncertainty. Any group trying to predict something like the climate with great certainty clearly doesn't understand or hasn't included many factors that would be very difficult to take account of with great accuracy.

That being said, a model with uncertainty will still produce ranges of likely outcomes within its own predictive limitations, and it is telling that - despite the increasing alignment between such models and likely 'reality' - the range of possible outcomes under an unabated emissions scenario (seemingly likely) continue to range from significant to downright alarming.

I wouldn't trust a climate model that doesn't have uncertainty. Any group trying to predict something like the climate with great certainty clearly doesn't understand or hasn't included many factors that would be very difficult to take account of with great accuracy.

That being said, a model with uncertainty will still produce ranges of likely outcomes within its own predictive limitations, and it is telling that - despite the increasing alignment between such models and likely 'reality' - the range of possible outcomes under an unabated emissions scenario (seemingly likely) continue to range from significant to downright alarming.

My thoughts are that it goes from scary short term to crazy catastrophic long term...there is little real controversy but the time it takes to get there. I don't see countries trying to control themselves very much, with necessity being the mother of invention...few countries see need when the worst is centuries off and decades are small changes.

There needs to be more political need, more discussion and conscious choices.

the image above does not seem to indicate the level of uncertainty that the image has. i think it would aid there argument if they added this at least i would show openness and understanding the limitations of the results.

I wouldn't trust a climate model that doesn't have uncertainty. Any group trying to predict something like the climate with great certainty clearly doesn't understand or hasn't included many factors that would be very difficult to take account of with great accuracy.

That being said, a model with uncertainty will still produce ranges of likely outcomes within its own predictive limitations, and it is telling that - despite the increasing alignment between such models and likely 'reality' - the range of possible outcomes under an unabated emissions scenario (seemingly likely) continue to range from significant to downright alarming.

I'm not sure your confidence in "range of possible outcomes" is justified. That seems to imply some degree of linearity. For a counter scenario (note: I'm not making a prediction or implying this is likely, just providing a plausible counter-example) consider increasing temperatures cause northern hemisphere deglaciation and the melt water shuts down the Atlantic conveyor, which moderates temperature in the northern hemisphere. Should that happen the northern hemisphere would quickly swing into a new ice age—quite the opposite of the "pop" conception of "climate change". This would probably fall outside of what you would consider the range of, if not "possible", but "probable" outcomes. I guess that's why climate scientists prefer the term "climate change" to "global warming".

An article on weather and no mention of chaos theory? Weather is a classic example of a chaotic system. Small changes in inputs can have big effects later on, while large changes don't necessarily change the system much.

You can refine the models and find generalized patterns, but you'll never be able to determine the specific weather forecast much into the future.

Weather isn't climate. Now, doubtless there ARE some dependencies on initial conditions in the climate as well, but my bet is that they will tend to damp out. Any starting conditions that are reasonable should fall within the same phase space.

No, the problem here is one of uncertainty about the different feedbacks. There are many of them, they are interrelated in complex ways, and small differences in the way they are modeled can result in fairly large differences in regional predictions.

You don't seem understand what he's saying. He's not saying that weather is climate, or vice-versa. He's saying that both weather and climate are chaotic systems resulting from the very complex non-linear feedbacks you, yourself, allude to.

He may be saying it, but it doesn't mean he is correct.

Aggregate systems averaged over long time periods often are not chaotic, simply because equilibriums are largely determined by things like the the total amount of energy in a system.

It's like the difference between trying to predict the exact motion of a gas particle, vs. predicting the pressure in a container using the ideal gas law.

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The problem in studying systems like these is not the underlying mechanisms—physics in this case—but these systems-level issues. Prediction is notoriously difficult because small changes in initial conditions lead to wide variation in output. Therefore these problems of increased uncertainty when the models have increasing sophistication.

That is not what is causing the increased uncertainties, it has nothing to do with sensitivities to initial conditions.

Quote:

It's interesting to note that models of chaotic systems with superior explanatory power may have weaker predictive power.

Edit: Chaotic systems don't "damp out" the effects of initial conditions, but the situation is not completely hopeless because of strange attractors. Note: planetary orbits are also chaotic but very predictable over the time frames we're normally interested in—we're currently in a relatively stable state space. The fear with climate is that we're forcing the climate out of a relatively stable state space into another. Whatever that new state space is, however, seems to have a high degree of uncertainty.

Yeah, I read that and frankly don't find it very compelling. Over long periods of time climate is, indeed a chaotic system. The assertions in this article, frankly, do not fill me with confidence if this is the prevailing opinion. I find the second comment to this article quite interesting.

I'm not arguing that predictions are impossible, or even probably accurate, but the wholesale dismissal of chaos theory quite unjustified. Non-linear feedbacks, as one sees in climate, almost always leads to chaotic behavior.

Yeah, I read that and frankly don't find it very compelling. Over long periods of time climate is, indeed a chaotic system. The assertions in this article, frankly, do not fill me with confidence if this is the prevailing opinion. I find the second comment to this article quite interesting.

I'm not arguing that predictions are impossible, or even probably accurate, but the wholesale dismissal of chaos theory quite unjustified. Non-linear feedbacks, as one sees in climate, almost always leads to chaotic behavior.

You are missing a sense of scale. Which processes are chaotic? On what scale? Can they be described by an average behavior on a larger scale?

Taking this ad absurdum would be to say that since the cloud processes don't take into account quantum mechanics, they can't possibly be simulating storm systems.(And this leaves out the fact that they demonstrate that skill by comparison with the real world.)And that statement, by the way, would preclude the effectiveness of any model of anything, from gravity to chemistry, to traffic.

Chaos theory would have you believe that one could predict when a toy boat released at the top of a watershed will enter the ocean.

Chaos theory was something of a nice idea ... but for predicting anything it hasn't been very useful.

We know the toy boat will enter the ocean and give the range of days it might occur, I think this is better than trying to sort out the infinite variables and somehow use math with an infinite matrix to find a pattern that will lead us to a definitive answer.

To make it simple: energy in = temperature equilibrium at some point in the future, keep adding CO2 keeps shifting the equilibrium. Feedback mechanism are hard to account for but most shift the equilibrium away from where it is today...it is more a matter of when the boat enters the ocean not if.

Well, it should be noted that chaotic systems don't equate to total chaos. There is plenty of predictability in weather and climate systems, but only up to a point, after which the model needs to be rerun with new observational data.

If you look at forecasted tracks for hurricane Sandy, the farther into the future you went, the less you could rely on the initial prediction, so they are constantly updating the models with new information, and even then, different models aren't going to agree.

Quote:

It's like the difference between trying to predict the exact motion of a gas particle, vs. predicting the pressure in a container using the ideal gas law.

Yeah, I read that and frankly don't find it very compelling. Over long periods of time climate is, indeed a chaotic system. The assertions in this article, frankly, do not fill me with confidence if this is the prevailing opinion. I find the second comment to this article quite interesting.

I'm not arguing that predictions are impossible, or even probably accurate, but the wholesale dismissal of chaos theory quite unjustified. Non-linear feedbacks, as one sees in climate, almost always leads to chaotic behavior.

Why do you characterize it as 'wholesale dismissal' rather than a justifiable understanding of the role chaos plays in weather forecasting (tons) vs. climate prediction (little)? Again, are you similarly annoyed at the application of the ideal gas law to calculate the volume of a balloon given that nobody can calculate the trajectory of each gas molecule?

What reference do you have to support your assertion that climate, over the long range,is a chaotic system? Why should we find your arguments persuasive at all when they seem to be merely arguments from incredulity? Over long time scales _weather_ is a chaotic system. Climate prediction is not weather prediction. I think this my be the crux of your issue.

Chaos theory would have you believe that one could predict when a toy boat released at the top of a watershed will enter the ocean.

That's not what chaos theory tell you at all. It has nothing to do with predicting the specific course of a toy boat.

Quote:

Chaos theory was something of a nice idea ... but for predicting anything it hasn't been very useful.

We know the toy boat will enter the ocean and give the range of days it might occur, I think this is better than trying to sort out the infinite variables and somehow use math with an infinite matrix to find a pattern that will lead us to a definitive answer.

It doesn't claim to be able to do that. As you say, you'd need infinite knowledge, and at that point you'd basically be God because you'd need to be omniscient.

It's a way of understanding how complex systems work, not predicting where a specific storm cell will form.

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To make it simple: energy in = temperature equilibrium at some point in the future, keep adding CO2 keeps shifting the equilibrium. Feedback mechanism are hard to account for but most shift the equilibrium away from where it is today...it is more a matter of when the boat enters the ocean not if.

The question shouldn't be when but what can we do to stop it.

That's kind of the point of weather, it's trying to equalize energy input from the sun. It's a side effect of sunlight. Add the spinning of the Earth and all the other factors and you get a very complicated system that doesn't follow linear maths.

The climate is aready changing rapidly:Arctic sea ice at lowest ever recorded levelHurricane KatrinaHurricane Sandy (both unprecedented)The midwest droughtEtc, etcYet climate didn't even get a single mention in any of the three presidential debates.Your nation is so out of touch it actually scares me (another eg your insane gun laws)

Climate prediction is not weather prediction. I think this my be the crux of your issue.

Climate builds on weather. They are not as unrelated as you want to make them.

You have that rather reversed. Weather builds on underlying climate. It's the 'noise' that rides on the climate 'signal'. You don't talk about climate changes on less than 'decadal' time scales, you don't predict weather more than ~10 days in advance. See the difference? They operate on completely different scales. Hurricane Sandy is not going to change the temperature 100 years from now, yet the temperature 100 years from now could have a very strong influence on the quantity and magnitude of the hurricanes.

Chaos theory would have you believe that one could predict when a toy boat released at the top of a watershed will enter the ocean.

Chaos theory was something of a nice idea ... but for predicting anything it hasn't been very useful.

We know the toy boat will enter the ocean and give the range of days it might occur, I think this is better than trying to sort out the infinite variables and somehow use math with an infinite matrix to find a pattern that will lead us to a definitive answer.

To make it simple: energy in = temperature equilibrium at some point in the future, keep adding CO2 keeps shifting the equilibrium. Feedback mechanism are hard to account for but most shift the equilibrium away from where it is today...it is more a matter of when the boat enters the ocean not if.

The question shouldn't be when but what can we do to stop it.

Chaotic systems are not what you think they are. I'm certain of that, actually, by your characterization of them as merely "something of a nice idea".

I think we can settle all this theoretical bickering over the role of chaos in physical theories with the following observation: there are many systems which exhibit chaotic motion at a low level, but which behave predictably at a larger scale. Examples of this are systems with large numbers of particles (the kinds of systems typically dealt with in statistical physics), certain fluids, where turbulence on small scales can be treated as viscosity on larger scales, and others which do not come to mind at the moment (most systems I can think of fall into the first category). The question is then whether weather and climate follow this same pattern, with weather behaving chaotically, but climate being insensitive to the precise initial conditions. I can certainly imagine that climate behaves the same on large enough timescales (decades) when the precise initial weather pattern is altered, provided certain macro variables, such as CO2 concentration, the distribution of different types of vegetation, etc. are not altered. While weather is sensitive to the momentary pattern of high- and low pressure regions, the long-term behavior of climate could well be insensitive to them.

This is not an article about weather, it is one about climate. Climate is long term averages including temperature, rain, snow and humidity over the seasons while weather is what is actually happening. It is possible to predict the climate years in advance but not weather.

It is currently thought that it is possible *in principle* to predict climate years in advance. These results show us that we are not there yet. If they were producing consistent results across the board, or if there were strong reasons to believe one over another, we could say that we *might* be there now.

What's worse: we may discover something, some detail of the math or a large sensitivity to random variations in a forcing function like solar weather or volcanism that makes all climate models unstable. I don't really believe that's what we'll find because the climate has been reasonably stable for quite a long time (10000 years or more) , which argues that we will be able to show a corresponding mathematical stability in the models.

But there are features we need to be looking for: we know there must be tipping points that cause ice ages to start and end and there may be other tipping points on the warm-temperature side and it darn well behooves us to know where they are and what life is like on the other side.

No, but probably they are predicting that many Americans are incapable of recognizing anything on the side of the planet that does not include their own continent.

So that's all it takes to get an up vote, now?

Back on topic, and to abstain from any sort of subtlety that the learned Ars community cannot seem to pick up on, my initial inquiry was mostly curious as to why the first three depictions were centered mainly on the North American continent, but the fourth was centered approximately on the Indochinese peninsula. Perhaps it doesn't matter prima facie, but is there a reason for not continuing a congruent representation? In more simple terminology, "one of these is not like the others." Why?

Yeah, I read that and frankly don't find it very compelling. Over long periods of time climate is, indeed a chaotic system. The assertions in this article, frankly, do not fill me with confidence if this is the prevailing opinion. I find the second comment to this article quite interesting.

I'm not arguing that predictions are impossible, or even probably accurate, but the wholesale dismissal of chaos theory quite unjustified. Non-linear feedbacks, as one sees in climate, almost always leads to chaotic behavior.

Why do you characterize it as 'wholesale dismissal' rather than a justifiable understanding of the role chaos plays in weather forecasting (tons) vs. climate prediction (little)? Again, are you similarly annoyed at the application of the ideal gas law to calculate the volume of a balloon given that nobody can calculate the trajectory of each gas molecule?

What reference do you have to support your assertion that climate, over the long range,is a chaotic system? Why should we find your arguments persuasive at all when they seem to be merely arguments from incredulity? Over long time scales _weather_ is a chaotic system. Climate prediction is not weather prediction. I think this my be the crux of your issue.

Well, you got me there. I'm not a climate scientist and can't provide you with specific references that I've read. However if you go to the linked article above and go to the second comment you'll see some references.

2) From what I've read about earth's climate over the course of time from scales of 10's of thousands of years to billions of years it exhibits all the hallmarks of a chaotic system: long periods stability punctuated by rapid transition to a new stable state.

So, basically, if it looks like a duck and quacks like a duck it may not be a duck, but it probably is a duck.

Edit: from the previous referenced link—

Quote:

First, the more appropriate scientific definition of climate is that it is a system involving the oceans, land, atmosphere and continental ice sheets with interfacial fluxes between these components, as we concluded in the 2005 National Research Council report. Observations show chaotic behavior of the climate system on all time scales, including sudden regime transitions, as we documented in Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox, H. Held, N. de Noblet-Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas, 2004: Nonlinearities, feedbacks and critical thresholds within the Earth’s climate system. Climatic Change, 65, 11-38.

also

Quote:

That climate is an integrated system and is sensitive to initial conditions is overviewed in Pielke, R.A., 1998: Long-term variability of climate. J. Atmos. Sci., 51, 155-159). We show in this study that even short-periodic natural variations of climate forcing can lead to significant long-term variability in the climate system.

So, the assertion is not without support and is, at least, credible, I think.

Climate prediction is not weather prediction. I think this my be the crux of your issue.

Climate builds on weather. They are not as unrelated as you want to make them.

You have it wrong. Weather builds on climate. Weather is the process by which heat (and other things, notably water) is exhanged between the parts of the world that climate makes hotter and the parts that climate makes cooler.

It seems that there might be some answers in information theory helping explain difficulties in modeling complex systems at ever increasing levels of detail. There may be very sound mathematical limits as to how much can be modeled and how detailed the models can be, clearly just adding more detailed variables isn't helping.

For practical use it would seem that reducing the number of variables modeled would help, just get it to 99 or 99.9% accurate and don't worry about the tiny 0.1% variables. Modeling just the big stuff will probably be of more use to policymakers anyway as long as you can roughly quantify how much you are leaving out.

This is not an article about weather, it is one about climate. Climate is long term averages including temperature, rain, snow and humidity over the seasons while weather is what is actually happening. It is possible to predict the climate years in advance but not weather.

It is currently thought that it is possible *in principle* to predict climate years in advance. These results show us that we are not there yet. If they were producing consistent results across the board, or if there were strong reasons to believe one over another, we could say that we *might* be there now.

What's worse: we may discover something, some detail of the math or a large sensitivity to random variations in a forcing function like solar weather or volcanism that makes all climate models unstable. I don't really believe that's what we'll find because the climate has been reasonably stable for quite a long time (10000 years or more) , which argues that we will be able to show a corresponding mathematical stability in the models.

But there are features we need to be looking for: we know there must be tipping points that cause ice ages to start and end and there may be other tipping points on the warm-temperature side and it darn well behooves us to know where they are and what life is like on the other side.

The climate is aready changing rapidly:Arctic sea ice at lowest ever recorded levelHurricane KatrinaHurricane Sandy (both unprecedented)The midwest droughtEtc, etcYet climate didn't even get a single mention in any of the three presidential debates.Your nation is so out of touch it actually scares me (another eg your insane gun laws)

The American presidential debates are a PR exercise run by a "commission" that is in reality a joint enterprise of the Democratic and the Republican parties. This so-called "commission" was established in 1987 by the Reps and Dems in lieu of accepting a more impartial arrangement -- the long-standing traditional debates conducted by the League of Women Voters.

Everything about those debates (format of the debate, lighting, which questions are/are not allowed to be addressed, whether there will be "follow-up" questions, who will moderate, whether the press will be allowed to "fact-check" or challenge statements, whether the candidates will/may address each other directly, etc, etc, etc.) are settled by intensive negotiations between those two political parties. If either candidate's team (or likely as not both of them, in the case of AGW/Climate Change) really doesn't want to deal with some issue, it just won't come up.

2) From what I've read about earth's climate over the course of time from scales of 10's of thousands of years to billions of years it exhibits all the hallmarks of a chaotic system: long periods stability punctuated by rapid transition to a new stable state.

So, basically, if it looks like a duck and quacks like a duck it may not be a duck, but it probably is a duck.

You are continuing to misunderstand. It is possible for a system to be chaotic and still have quite predictable overall statical properties. The specific phase space orbit of a nonlinear system may be very sensitive to the value of a given parameter (and as a result look dramatically different from another), but the statistics of various constituant variables may be very consistant. If one were, for example, to run an ensemble of simulations of a given nonlinear system by varying a given parameter or parameters between ensemble members, the specific resulting orbits may be drastically different, but the variance of the means of a given constituent variable across the ensemble might be quite low. Based upon the work done on climate simulation - and there is a ton of it (see the realclimate link that you so readily dismissed above for some links), not just because it's climate-related but because it is interesting and important math - the underlying dynamics of climate modeling seems to behave this way. If one of those constituant phase space variables is temperature (as it would be in an atmospheric simulation), different ensemble members with very different resulting orbits may nevertheless have very consistant mean temperatures. That is climate. The fact that the underlying dynamics are nonlinear is very important and actually reasonably well understood, but the overall statistics of those dynamics are also well understood and apparently rather consistant.

Edit: Do me a favor... If you are going to vote this post down, go ahead if you want to, but please post yourself as to what you think is wrong with it so that your criticism can be addressed. Don't just vote it down because you don't like it - that achieves nothing.